code: def forward(self, input, target):
      n = input.size(0)
      conf_mask = torch.gt(target, self.threshold).float()
      input_flat = input.view(n, -1)
      target_flat = target.view(n, -1)
      conf_mask_flat = conf_mask.view(n, -1)
      diff = (input_flat - target_flat) ** 2
      diff_conf = diff * conf_mask_flat
      loss = diff_conf.mean()
      return loss
  
file_path: medicaltorch/medicaltorch/losses.py
imports: import torch
  from torch.nn import Module
label_desc: Use image node_id 2 for calling the ConfidentMSELoss obj's underlying member method code with example usage: obj.forward(input, target) and returns: loss